title: Database queries for hospitalizations for acute congestive heart failure: flexible methods and validation based on set theory.
abstract: OBJECTIVE
  Electronic health records databases are increasingly used for identifying cohort populations, covariates, or outcomes, but discerning such clinical 'phenotypes' accurately is an ongoing challenge. We developed a flexible method using overlapping (Venn diagram) queries. Here we describe this approach to find patients hospitalized with acute congestive heart failure (CHF), a sampling strategy for one-by-one 'gold standard' chart review, and calculation of positive predictive value (PPV) and sensitivities, with SEs, across different definitions.
  
  
  METHODS
  We used retrospective queries of hospitalizations (2002-2011) in the Indiana Network for Patient Care with any CHF ICD-9 diagnoses, a primary diagnosis, an echocardiogram performed, a B-natriuretic peptide (BNP) drawn, or BNP >500 pg/mL. We used a hybrid between proportional sampling by Venn zone and over-sampling non-overlapping zones. The acute CHF (presence/absence) outcome was based on expert chart review using a priori criteria.
  
  
  RESULTS
  Among 79,091 hospitalizations, we reviewed 908. A query for any ICD-9 code for CHF had PPV 42.8% (SE 1.5%) for acute CHF and sensitivity 94.3% (1.3%). Primary diagnosis of 428 and BNP >500 pg/mL had PPV 90.4% (SE 2.4%) and sensitivity 28.8% (1.1%). PPV was <10% when there was no echocardiogram, no BNP, and no primary diagnosis. 'False positive' hospitalizations were for other heart disease, lung disease, or other reasons.
  
  
  CONCLUSIONS
  This novel method successfully allowed flexible application and validation of queries for patients hospitalized with acute CHF.
authors.affiliation: Children's Health Services Research, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
authors.forename: Marc
authors.initials: M
authors.lastname: Rosenman
authors.forename: Jinghua
authors.initials: J
authors.lastname: He
authors.forename: Joel
authors.initials: J
authors.lastname: Martin
authors.forename: Kavitha
authors.initials: K
authors.lastname: Nutakki
authors.forename: George
authors.initials: G
authors.lastname: Eckert
authors.forename: Kathleen
authors.initials: K
authors.lastname: Lane
authors.forename: Irmina
authors.initials: I
authors.lastname: Gradus-Pizlo
authors.forename: Siu L
authors.initials: SL
authors.lastname: Hui
mesh_terms: D000208:Acute Disease; D057286:Electronic Health Records; D006333:Heart Failure; D006760:Hospitalization; D006801:Humans; D007196:Indiana; D033181:Information Dissemination; D016247:Information Storage and Retrieval; D038801:International Classification of Diseases; D008498:Medical Record Linkage; D012189:Retrospective Studies
keywords: Algorithms; Electronic Health Records; Heart Failure; Phenotypes; Predictive Value of Tests; Validation Studies